Big Data Analytics


The large volume of data in either structured or unstructured formats are referred as Big Data. The five degrees defining big data are volume, variety, value, veracity and velocity. Velocity refers to the speed of generation of data. Volume is the amount of the accumulated data. Variety is the types and dimensions of data available. Value is the worth of the data and its importance in further working. Lastly, veracity defines the quality, precision and the reliability of the data. With the increase in innovation, and internet usage has generated the requirement of handling large amount of data.


Big Data analytics is a term to define the very complex method of examining a variety of large data sets or commonly known as big data. This method includes uncovering and decrypting the hidden patterns, unknown correlations, trends, categorization, and handling of data to be used as useful information by organizations in making decisions or working.

The data analytics technologies and methods provide a medium to analyze the data set and sort them for making further conclusions. It is an advanced form of analytics which includes very complicated application and elements, algorithms to prepare analysis and conclusion for high-perform of organizations.

Working of Big Data Analysis:

In a few cases, systems like Hadoop clusters and NoSQL are utilized as the initial base for landing and accumulation of data before it is departed to the analytical database and data warehouse for further analysis and handling. This helps in making the data more structured and organized for further working.

Recently, it is noted that big data analytics works are choosing Hadoop data lake, which acts as the initial source for incoming raw large data. In such structures, the data can be examined directly in Hadoop cluster or processed in the engine like Spark.

After the data is organized, configured properly, it becomes ready for further analytics processes. These processes include data mining, predictive analytics, machine learning, deep learning, text mining, statistical analysis, and a few others. These processes make sure to sort and handle data in the most efficient form with best possible conclusions.


One of the most valuable revolutions in IT is big data analytics. The requirement of big data analytics has only been increasing since its inception. The primary focus of any organization or companies or any website is its visitors or customers. Therefore, to flourish the business to a consumer application, big data and big data analytics hold immense importance. It helps the business to improve its consumer interaction, enhance their potential, reliability, capability and the list goes on. The three broad divisions of big data analytics are descriptive analytics, prescriptive analytics and predictive analytics.

These analytics further expand the companies’ vision to improved working and consumer satisfaction. This also helps in reducing the cost of handling information with efficient access and meaningful utilization of data for better business or organization decisions. Efficiency hence, makes the working easier, saves time and energy, which also aids the business to expand more conveniently.

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